{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpath = './Ames_house/'\n",
    "data = pd.read_csv(dpath + 'Ames_House_train.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                  0\n",
       "MSSubClass          0\n",
       "MSZoning            0\n",
       "LotFrontage       259\n",
       "LotArea             0\n",
       "Street              0\n",
       "Alley            1369\n",
       "LotShape            0\n",
       "LandContour         0\n",
       "Utilities           0\n",
       "LotConfig           0\n",
       "LandSlope           0\n",
       "Neighborhood        0\n",
       "Condition1          0\n",
       "Condition2          0\n",
       "BldgType            0\n",
       "HouseStyle          0\n",
       "OverallQual         0\n",
       "OverallCond         0\n",
       "YearBuilt           0\n",
       "YearRemodAdd        0\n",
       "RoofStyle           0\n",
       "RoofMatl            0\n",
       "Exterior1st         0\n",
       "Exterior2nd         0\n",
       "MasVnrType          8\n",
       "MasVnrArea          8\n",
       "ExterQual           0\n",
       "ExterCond           0\n",
       "Foundation          0\n",
       "                 ... \n",
       "BedroomAbvGr        0\n",
       "KitchenAbvGr        0\n",
       "KitchenQual         0\n",
       "TotRmsAbvGrd        0\n",
       "Functional          0\n",
       "Fireplaces          0\n",
       "FireplaceQu       690\n",
       "GarageType         81\n",
       "GarageYrBlt        81\n",
       "GarageFinish       81\n",
       "GarageCars          0\n",
       "GarageArea          0\n",
       "GarageQual         81\n",
       "GarageCond         81\n",
       "PavedDrive          0\n",
       "WoodDeckSF          0\n",
       "OpenPorchSF         0\n",
       "EnclosedPorch       0\n",
       "3SsnPorch           0\n",
       "ScreenPorch         0\n",
       "PoolArea            0\n",
       "PoolQC           1453\n",
       "Fence            1179\n",
       "MiscFeature      1406\n",
       "MiscVal             0\n",
       "MoSold              0\n",
       "YrSold              0\n",
       "SaleType            0\n",
       "SaleCondition       0\n",
       "SalePrice           0\n",
       "Length: 81, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>...</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>...</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
       "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
       "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
       "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000   \n",
       "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726   \n",
       "std       1.112799    30.202904     20.645407   181.066207   456.098091   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000   \n",
       "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000   \n",
       "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000   \n",
       "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000   \n",
       "\n",
       "           ...         WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  \\\n",
       "count      ...        1460.000000  1460.000000    1460.000000  1460.000000   \n",
       "mean       ...          94.244521    46.660274      21.954110     3.409589   \n",
       "std        ...         125.338794    66.256028      61.119149    29.317331   \n",
       "min        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "25%        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "50%        ...           0.000000    25.000000       0.000000     0.000000   \n",
       "75%        ...         168.000000    68.000000       0.000000     0.000000   \n",
       "max        ...         857.000000   547.000000     552.000000   508.000000   \n",
       "\n",
       "       ScreenPorch     PoolArea       MiscVal       MoSold       YrSold  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1460.000000  1460.000000   \n",
       "mean     15.060959     2.758904     43.489041     6.321918  2007.815753   \n",
       "std      55.757415    40.177307    496.123024     2.703626     1.328095   \n",
       "min       0.000000     0.000000      0.000000     1.000000  2006.000000   \n",
       "25%       0.000000     0.000000      0.000000     5.000000  2007.000000   \n",
       "50%       0.000000     0.000000      0.000000     6.000000  2008.000000   \n",
       "75%       0.000000     0.000000      0.000000     8.000000  2009.000000   \n",
       "max     480.000000   738.000000  15500.000000    12.000000  2010.000000   \n",
       "\n",
       "           SalePrice  \n",
       "count    1460.000000  \n",
       "mean   180921.195890  \n",
       "std     79442.502883  \n",
       "min     34900.000000  \n",
       "25%    129975.000000  \n",
       "50%    163000.000000  \n",
       "75%    214000.000000  \n",
       "max    755000.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115861f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.distplot(data.SalePrice.values, bins=30, kde=True)\n",
    "plt.xlabel('Sale Price', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115d10198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(range(data.shape[0]), data['SalePrice'].values, color='purple')\n",
    "plt.title('Distribution of Price');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1451, 81)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[data.SalePrice < 500000]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
